Optimal manufacturing–remanufacturing policies in a lean production environment

This study analyses a production-management model that considers the possibility of implementing a reverse-logistics system for remanufacturing end-of-life products in a lean production environment (as opposed to models that use EOQ approaches). Decision variables are identified (including manufacturing and remanufacturing capacities and return rates and use rates for end-of-life products) and optimal policies are determined. Moreover, the structure of these optimal policies is analysed. The conclusion drawn is that, in many realistic scenarios, mixed policies (that is, with return rates and use rates strictly between 0 and 1) can be optimal. This conclusion is contrary to results published in earlier studies, which are based on more restrictive assumptions.     

The good work--a Swedish trade union vision in the shadow of lean production

“The Good Work” (Det goda arbetet) was established as a highly praised and established concept in the Swedish working life debate in the middle of the 1980s. In this paper, we are going to discuss the concept in relation to the massive introduction of lean production in Swedish industry. The aim of this paper is to restore the theory of the good work into the industrial society of today. We will search for a model for ‘good work’ in balance between the demands from production and good conditions for a learning environment.The theoretical base for this paper will be found in both organisational research and research on production technology systems. We identify three strong trends in Swedish industrial companies giving both pitfalls and possibilities for the good work; the learning focus as a way to increase productivity and improve working conditions; Lean Production in most cases imply narrow short-cyclic work tasks; and the global market that reduces national discretion. As a result, we formulate a new set of criteria for “the good work”.     

The Operations-Time Chart: A graphical tool to evaluate the performance of production systems – From batch-and-queue to lean manufacturing

In this paper, we describe the spreadsheet modeling of manufacturing systems by means of the Operations-Time Chart (hereafter, OT-Chart), a graphical tool for an automatic time-phased representation and measurement of the operation of production systems, developed by the authors. In order to improve the design of a production system, it is necessary to know the key performance metrics of the system (productivity, lead-time, inventories, downtimes and wait times) and identify the effects of design parameters on system performance. Calculating some of these magnitudes can be very complicated, especially for production systems involving multiple and confluent processes, with different cycle times and lot sizes. The OT-Chart permits a visual tracking of the aforementioned parameters throughout each process, and like a simulation tool, the program calculates and displays the effects of changing input parameters. A special version of the Chart has been designed for lean manufacturing environments, where visual tools are much appreciated. The OT-Chart provides tracking of different types of waste and supports inventory supermarkets and pull scheduling. The paper includes a case study: a plant is redesigned from a conventional batch-and-queue production system into a lean manufacturing system with the help of the OT-Chart (it is used to test the performance of each layout) allowing managers to evaluate and refine their designs.     

Strategies of performance self-monitoring in automotive production

Production in the automotive industry, based on assembly line work, is now characterized by lean manufacturing and customization. This results in greater flexibility and increased quality demands, including worker performance self-monitoring. The objectives of this study are to refine the concept of performance self-monitoring and to characterize the strategies developed by operators to achieve it. Data were collected based on the method of individual auto-confrontation, consisting of two steps: eleven assembly-line operators of a French automotive company were individually observed and video-taped while they were working; an interview then allowed each operator to discuss his/her activity based on the video-tape. This study expands the concept of performance self-monitoring by highlighting three types of strategies directly oriented toward quality: prevention, feedback control and control action strategies.     

Fuzzy discrete event simulation: A new tool for rapid analysis of production systems under vague information

Information availability is often a critical point for the application of classical quantitative techniques for production system analysis. In several production problems, indeed, managers have considered the possibility to incorporate expert estimates of production data into a model as a substitute for hard data as very interesting. Furthermore, the use of qualitative representations to model physical systems is attractive because it allows to capture the inherent ambiguity characterizing real systems. Fuzzy set theory allows to gain such attractive options, since it provides tools to process vague information.This paper concerns the new and interesting topic of fuzzy discrete event simulation. In particular, the problem of processing fuzzy data within a discrete event simulation process is discussed and new methods, able to limit time paradox problems, are proposed. Furthermore, the paper addresses the comparison among fuzzy and classical simulation, pointing out peculiarities, potentialities and application fields of such a new tool; finally, the research highlights the necessity to develop proper and specific fuzzy simulation packages by demonstrating that, even under specific and simplifying hypotheses about process uncertainties, fuzzy simulation analysis results cannot be obtained through classical simulation packages.     

Autonomy in production logistics: Identification, characterisation and application

Competitive enterprises have to react fast and flexible to an increasing dynamic environment. To achieve the ability to adapt on these new requirements autonomous cooperating logistic processes seem to be an appropriate method. In order to prove in which case autonomously controlled processes are more advantageous than conventionally managed processes, it is essential to specify what is exactly meant with autonomous control, how autonomous control does differ from conventional control and how the achievement of logistic objectives in autonomously controlled systems can be estimated and compared to the achievement of objectives in conventionally controlled systems. This paper introduces a general definition of autonomous control as well as a definition in the context of engineering science and its meaning in a logistics context. Based on this, a catalogue of criteria is developed to ensure the identification of autonomous cooperating processes in logistic systems and its distinction to conventionally controlled processes. To demonstrate this catalogue, its criteria and the concerning properties are explained by means of an exemplary shop-floor scenario.     

Data mining in design of products and production systems

Data mining is acquiring its own identity by refining concepts from other disciplines, developing generic algorithms, and entering new application areas. Engineering design and manufacturing have been affected by the data mining pursuit. This paper outlines areas of product and manufacturing system design that are particularly suitable for data-mining applications. One of the emerging areas is innovation. The key challenges of data mining in the domains discussed in the paper are outlined.     

How to decentralize decisions through GRAI model in production management

Our Laboratory has been working in the field of Production Management for over a decade. Our research has been made in a pragmatic way, alternating analyses on the ground, research for solutions to formulated problems and experiments of possible solutions back on the ground.We have particularly looked into the structure of Production Management Systems (PMS) because we have noticed the lack in existing of a global model and the difficulty in drawing up some specifications for these systems.During our research of a global structure, we have been quickly faced with a lack of PMS efficiency in driving the physical system. The complexity of this structure led us to split it up into more and more simple elements drawing our approach from MELESE concepts [1].But ASHBY concepts dealing with variety have led us to split up again and to decentralize decision centers in order to make possible the driving of PMS [2].In this paper we will present the model which has allowed us to design a specific PMS organized into a hierarchy with a decentralized decision center.     

Improvement of quality performance in manufacturing organizations by minimization of production defects

The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process attribute chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks’ study after implementing changes, there was a 9% reduction in defects.     

Multiple lot-sizing decisions with an interrupted geometric yield and variable production time

This study examines a multiple lot-sizing problem for a single-stage production system with an interrupted geometric distribution, which is distinguished in involving variable production lead-time. In a finite number of setups, this study determined the optimal lot-size for each period that minimizes total expected cost. The following cost items are considered in optimum lot-sizing decisions: setup cost, variable production cost, inventory holding cost, and shortage cost. A dynamic programming model is formulated in which the duration between current time and due date is a stage variable, and remaining demand and work-in-process status are state variables. This study then presents an algorithm for solving the dynamic programming problem. Additionally, this study examines how total expected costs of optimal lot-sizing decisions vary when parameters are changed. Numerical results show that the optimum lot-size as a function of demand is not always monotonic.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

Distributed control of production systems

This editorial introduces the special issue of the Elsevier journal, Engineering Application of Artificial Intelligence, on Distributed control of production systems. The current technology in communication and embedded systems allows products and production resources to play a more active role in the production process. This new active capacity will generate major changes in organizations and information systems (e.g., Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES)). New approaches are now required for modelling, testing and assessing the features made possible by the decisional and informational capabilities of these new active entities. One among the many possibilities is to use agents and holons, since agent and holon-based approaches assume interaction between intelligent entities to facilitate the emergence of a global behavior. This special issue thus focuses on the possible applications of distributed approaches for the design, evaluation and implementation of new control architectures for production systems. Both fundamental and applied research papers are presented.     

Mean station reliabilities cause throughput overestimates in production system design

In production system design, we typically lack actual station reliability data for throughput analysis using a model, because these stations do not yet exist. Hence, we either use the mean values from similar existing equipment or mean reliability estimates provided by equipment manufacturers. The real stations may have better or worse reliabilities compared to the means. Hence, when the system is built, the real system throughput may be acceptable or poor depending on actual station reliabilities. This paper compares predicted model throughput using station mean reliabilities with real system throughput. We find that the model often overestimates system throughput. We develop an upper bound and the maximum probability of overestimation when there is an infinite buffer size after each station. We also provide the ranges of overestimation for systems with limited buffers. These results may be used as a “rule of thumb” to adjust system throughput estimation. Monte Carlo simulation is discussed as an approach to analyze real system performance.     

ManufAg: a multi-agent-system framework for production control of complex manufacturing systems

In this paper, we present a framework for the implementation of multi-agent-systems for production control of complex manufacturing systems. We present the results of a requirement analysis for production control systems for complex manufacturing systems; then we describe the framework design criteria. Our framework supports the inclusion of distributed hierarchical decision-making schemes into the production control. Furthermore, in order to increase the coordination abilities of multi-agent-systems, we follow the decision-making and staff agent architecture suggested in the PROSA reference architecture. We indicate the usage of the framework for designing and implementing an agent-based production control system for semiconductor manufacturing processes in a case study.     

Probabilistic approximations of ODEs based bio-pathway dynamics

Bio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network.This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency.     

A multi-objective customer orders assignment and resource leveling in make-to-order manufacturing

This paper presents integer programming formulations and compares two approaches – weighting and lexicographic – to the multi‐objective, long‐term production scheduling in make‐to‐order manufacturing, where both maximization of customer service level and best utilization of production resources are integrated in the objective function. The problem objective is to assign customer orders for various product types and with various due dates to planning periods and to select machines for assignment in every period to complete all the orders with minimum numbers of tardy and early orders and with a leveled machine assignment over a planning horizon. The two approaches are applied to optimize long‐term production schedules in a flexible flowshop with parallel machines. Numerical examples modeled after a real‐world flexible assembly line in the electronics industry are provided and some computational results are reported.     

Stochastic model of production and inventory control using dynamic bayesian network

Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Simultaneous analysis of production and investment performance of Canadian life and health insurance companies using data envelopment analysis

A new data envelopment analysis (DEA) model is created to provide valuable managerial insights when assessing the dual impacts of operating and business strategies for Canadian life and health (L&H) insurance industry. This problem-oriented new DEA model can simultaneously assess the production and investment performance of insurers, differing from classical DEA models appropriate for independent performance evaluation. The mathematical solution is provided for this new model and the results show that the Canadian L&H insurance companies operated very efficiently for the examined 3-year period (1996–1998). In addition, no scale efficiency in the Canadian L&H insurance industry is found in this study.     

Markov decision models for the optimal maintenance of a production unit with an upstream buffer

We consider a manufacturing system in which a buffer has been placed between the input generator and the production unit. The input generator supplies at a constant rate the buffer with the raw material, which is pulled by the production unit. The pull-rate is greater than the input rate when the buffer is not empty. The two rates become equal as soon as the buffer is evacuated. The production unit deteriorates stochastically over time and the problem of its optimal preventive maintenance is considered. Under a suitable cost structure it is proved that the optimal average-cost policy for fixed buffer size is of control-limit type, if the repair times are geometrically distributed. Efficient Markov decision process solution algorithms that operate on the class of control-limit policies are developed, when the repair times are geometrical or follow a continuous distribution. The optimality of a control-limit policy is also proved when the production unit after the end of a maintenance remains idle until the buffer is filled up. Furthermore, numerical results are given for the optimal policy if it is permissible to leave the production unit idle whenever it is in operative condition.     

Manufacturing production plan optimization in three-stage supply chains under Bass model market effects

The Bass model is a very successful parametric approach to forecast the diffusion process of new products. In recent years, applications of the Bass model have been extended to other operational research fields such as managing customer demands, controlling inventory levels, optimizing advertisement strategies, and so forth. This study attempts to establish an application for optimizing manufacturers’ production plans in a three-stage supply chain under the Bass model’s effects on the market. The supply chain structure considered in this research is similar to other common supply chains comprised of three stages, namely retailer, distributor and manufacturer. The retailer stage has to handle customer demands following the Bass diffusion process. Market parameters and essential information are assumed to be available and ready for access. Each stage is expected to determine its inventory policy rationally. That is, each stage will attempt to maximize its own profits. These decisions will back-propagate their effects to upper stages. This study adopts a dynamic programming approach to determine the inventory policies of each stage so as to optimize manufacturers’ production plans.